
# uncomment below lines of code to install required packages
# !pip install dash

# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install plotly_express
# !pip install dfply


import base64
from base64 import b64encode
import datetime
import io

import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_table
from dash.dependencies import Input, Output

from collections import OrderedDict

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
import seaborn as sns 
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
# import plotnine

import dfply
from dfply import *

from plotnine import ggplot, aes

# package for plot scales
from mizani.formatters import comma_format

import ipywidgets as widgets

import warnings
warnings.filterwarnings('ignore') 

from IPython.display import display


# Loading data (sedchem) from GitHub
# May be changed as per the individual repository
data = pd.read_csv('https://raw.githubusercontent.com/vinthegreat84/geochemistry/master/SedWeather/raw/sedchem.csv')


data['molar_SiO2']  = data['SiO2']/60.08
data['molar_TiO2']  = data['TiO2']/79.866
data['molar_Al2O3'] = data['Al2O3']/101.9618
data['molar_Fe2O3'] = data['Fe2O3']/159.69
data['molar_MnO']   = data['MnO']/70.9374
data['molar_MgO']   = data['MgO']/40.3044
data['molar_CaO']   = data['CaO']/56.0794
data['molar_Na2O']  = data['Na2O']/61.97894
data['molar_K2O']   = data['K2O']/94.1954
data['molar_P2O5']  = data['P2O5']/141.944522
data['molar_CO2']   = data['CO2']/44.01


# After McLennan, 1993
data['diff'] = data['molar_CaO'] - (data['molar_CO2'] + data['molar_P2O5'])
data['molar_CaO*'] = np.where(data['diff'] < data['molar_Na2O'], data['diff'], data['molar_Na2O'])


# Chemical Index of Weathering (CIW) after Harnois, 1988
data['(CIW)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O']) 

# Chemical Proxy of Alteration (CPA) after Buggle et al., 2011  
data['(CPA)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_Na2O']) 

# Chemical Index of Alteration (CIA) after Nesbitt and Young, 1982
data['(CIA)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O']) 

# Plagioclase Index of Alteration (PIA) after Fedo et al., 1995
data['(PIA)'] = 100 * (data['molar_Al2O3'] - data['molar_K2O']) / (data['molar_Al2O3'] + data['molar_CaO*'] + data['molar_Na2O'] 
                                                                   - data['molar_K2O'])

# Modified Chemical Index of Alteration (CIX) after Garzanti et al., 2014                                                            
data['(CIX)'] = 100 * data['molar_Al2O3'] / (data['molar_Al2O3'] + data['molar_Na2O'] + data['molar_K2O']) 

# Index of Compositional Variability (ICV) after Cox et al., 1995
data['(ICV)'] = (data['Fe2O3'] + data['K2O'] + data['Na2O'] + data['CaO'] + data['MgO'] + data['MnO'] + data['TiO2']) / data['Al2O3']

# Weathering Index of Parker (WIP) after Parker, 1970
data['(WIP)'] = 100 * (2*data['molar_Na2O']/0.35 + data['molar_MgO']/0.9 + 2*data['molar_K2O']/0.25 + data['molar_CaO*']/0.7)


# Category plot
ACNK_cat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'], c=data['molar_K2O'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_cat.update_layout({'ternary': {'sum': 100}})
ACNK_cat.update_ternaries(bgcolor='yellow')
ACNK_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_cat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_cat.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_cat.write_html("category plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_cat, image='svg', filename='category plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_cat, image='jpeg', filename='category plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory plot
ACNK_subcat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'], c=data['molar_K2O'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_subcat.update_layout({'ternary': {'sum': 100}})
ACNK_subcat.update_ternaries(bgcolor='yellow')
ACNK_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_subcat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_subcat.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_subcat.write_html("subcategory plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_subcat, image='svg', filename='subcategory plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_subcat, image='jpeg', filename='subcategory plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version


# eight oxides (SiO2, TiO2, Al2O3, Fe2O3, MgO, CaO, Na2O and K2O) used in the formulas and re-closing to 100 wt.% (expressed as OXIDE.100 = 100*OXIDE/sum)
data['sum'] = data[['SiO2', 'TiO2', 'Al2O3', 'Fe2O3', 'MgO', 'CaO', 'Na2O', 'K2O']].sum(axis=1)

data['SiO2.100']  = 100 * data['SiO2']  / data['sum']
data['TiO2.100']  = 100 * data['TiO2']  / data['sum']
data['Al2O3.100'] = 100 * data['Al2O3'] / data['sum']
data['Fe2O3.100'] = 100 * data['Fe2O3'] / data['sum']
data['MgO.100']   = 100 * data['MgO']   / data['sum']
data['CaO.100']   = 100 * data['CaO']   / data['sum']
data['Na2O.100']  = 100 * data['Na2O']  / data['sum']
data['K2O.100']   = 100 * data['K2O']   / data['sum']

# M - F - W apices calculation after Ohta and Arai, 2007
## Step 1 of the formulas for calculating vertices (M, F and W) of M - F - W compositional space diagram
data['(M)'] = - 0.395*np.log(data['SiO2.100']) + 0.206*np.log(data['TiO2.100']) - 0.316*np.log(data['Al2O3.100']) + 0.160*np.log(data['Fe2O3.100']) + 0.246*np.log(data['MgO.100']) + 0.368*np.log(data['CaO.100']) + 0.073*np.log(data['Na2O.100']) - 0.342*np.log(data['K2O.100']) + 2.266
data['(F)'] = + 0.191*np.log(data['SiO2.100']) - 0.397*np.log(data['TiO2.100']) + 0.020*np.log(data['Al2O3.100']) - 0.375*np.log(data['Fe2O3.100']) - 0.243*np.log(data['MgO.100']) + 0.079*np.log(data['CaO.100']) + 0.392*np.log(data['Na2O.100']) + 0.333*np.log(data['K2O.100']) - 0.892
data['(W)'] = + 0.203*np.log(data['SiO2.100']) + 0.191*np.log(data['TiO2.100']) + 0.296*np.log(data['Al2O3.100']) + 0.215*np.log(data['Fe2O3.100']) - 0.002*np.log(data['MgO.100']) - 0.448*np.log(data['CaO.100']) - 0.464*np.log(data['Na2O.100']) + 0.008*np.log(data['K2O.100']) - 1.374

## Step 2 of the formulas for calculating vertices (M, F and W) of M - F - W compositional space diagram
data['(M)'] = np.exp(data['(M)'])
data['(F)'] = np.exp(data['(F)'])
data['(W)'] = np.exp(data['(W)'])

# ternary plot drawing
MFW_cat = px.scatter_ternary(data, a=data['(M)'], b=data['(F)'], c=data['(W)'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_cat.update_layout({'ternary': {'sum': 100}})
MFW_cat.update_ternaries(bgcolor='yellow')
MFW_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_cat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_cat.show()

# uncomment below lines of code to export the plot to your local machine
# MFW_cat.write_html("category plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_cat, image='svg', filename='category plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_cat, image='jpeg', filename='category plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory plot
MFW_subcat = px.scatter_ternary(data, a=data['(M)'], b=data['(F)'], c=data['(W)'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_subcat.update_layout({'ternary': {'sum': 100}})
MFW_subcat.update_ternaries(bgcolor='yellow')
MFW_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_subcat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_subcat.show()

# uncomment below lines of code to export the plot to your local machine
# MFW_subcat.write_html("subcategory plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_subcat, image='svg', filename='subcategory plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_subcat, image='jpeg', filename='subcategory plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Category plot
ACNKFM_cat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O'], c=data['molar_Fe2O3'] + data['molar_MgO'], color = data['category'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_cat.update_layout({'ternary': {'sum': 100}})
ACNKFM_cat.update_ternaries(bgcolor='yellow')
ACNKFM_cat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_cat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_cat.show()

# uncomment below lines of code to export the plot to your local machine
# ACNKFM_cat.write_html("category plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_cat, image='svg', filename='category plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_cat, image='jpeg', filename='category plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory plot
ACNKFM_subcat = px.scatter_ternary(data, a=data['molar_Al2O3'], b=data['molar_CaO*'] + data['molar_Na2O'] + data['molar_K2O'], c=data['molar_Fe2O3'] + data['molar_MgO'], color = data['subcategory'], symbol = data['subsubcategory'], hover_name = data['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_subcat.update_layout({'ternary': {'sum': 100}})
ACNKFM_subcat.update_ternaries(bgcolor='yellow')
ACNKFM_subcat.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_subcat.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_subcat.show()

# uncomment below lines of code to export the plot to your local machine
# ACNKFM_subcat.write_html("subcategory plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_subcat, image='svg', filename='subcategory plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_subcat, image='jpeg', filename='subcategory plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# filtering data into subcategories

# filtering data into subcategory (upper_continental_crust)
data_upper_continental_crust = data[data.subcategory == 'upper_continental_crust']

# filtering data into subcategory (igneous_composite)
data_igneous_composite = data[data.subcategory == 'igneous_composite']

# filtering data into subcategory (shale_composite)
data_shale_composite = data[data.subcategory == 'shale_composite']

# filtering data into subcategories (UCC, river_sediment, region)  
data_UCC_rs_r = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == 'river_sediment') | (data["subcategory"] == 'region')]

# filtering data into subcategory (UCC, Manasbal_Lake)
data_UCC_ml = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Manasbal_Lake")]

# filtering data into subcategory (UCC, Surma_Group, Barail_Group)
data_UCC_sg_bg = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Surma_Group") | (data["subcategory"] == "Barail_Group")]

# filtering data into subcategory (UCC, channel_sediments, overbank_sediments, suspended_sediments)
data_UCC_cs_os_ss = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "channel_sediments") | (data["subcategory"] == "overbank_sediments") | (data["subcategory"] == "suspended_sediments")]

# filtering data into subcategory (UCC, Silty_clay, Sand)
data_UCC_sc_s = data[(data["subcategory"] == 'upper_continental_crust') | (data["subcategory"] == "Silty_clay") | (data["subcategory"] == "Sand")]


# Subcategory ('UCC') plots

# A - CN - K compositional space diagram
ACNK_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['molar_Al2O3'], b=data_upper_continental_crust['molar_CaO*'] + data_upper_continental_crust['molar_Na2O'], c=data_upper_continental_crust['molar_K2O'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
ACNK_upper_continental_crust.update_ternaries(bgcolor='yellow')
ACNK_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_upper_continental_crust.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_upper_continental_crust.show()

# M - F - W compositional space diagram
MFW_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['(M)'], b=data_upper_continental_crust['(F)'], c=data_upper_continental_crust['(W)'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
MFW_upper_continental_crust.update_ternaries(bgcolor='yellow')
MFW_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_upper_continental_crust.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_upper_continental_crust.show()


# A - CNK - FM compositional space diagram
ACNKFM_upper_continental_crust = px.scatter_ternary(data_upper_continental_crust, a=data_upper_continental_crust['molar_Al2O3'], b=data_upper_continental_crust['molar_CaO*'] + data_upper_continental_crust['molar_Na2O'] + data_upper_continental_crust['molar_K2O'], c=data_upper_continental_crust['molar_Fe2O3'] + data_upper_continental_crust['molar_MgO'], color = data_upper_continental_crust['subcategory'], symbol = data_upper_continental_crust['subsubcategory'], hover_name = data_upper_continental_crust['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_upper_continental_crust.update_layout({'ternary': {'sum': 100}})
ACNKFM_upper_continental_crust.update_ternaries(bgcolor='yellow')
ACNKFM_upper_continental_crust.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_upper_continental_crust.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_upper_continental_crust.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_upper_continental_crust.write_html("upper_continental_crust plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_upper_continental_crust, image='svg', filename='upper_continental_crust plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_upper_continental_crust.write_html("upper_continental_crust plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_upper_continental_crust, image='svg', filename='upper_continental_crust plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_upper_continental_crust.write_html("upper_continental_crust plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_upper_continental_crust, image='svg', filename='upper_continental_crust plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_upper_continental_crust, image='jpeg', filename='upper_continental_crust plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('igneous_composite') plots

# A - CN - K compositional space diagram
ACNK_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['molar_Al2O3'], b=data_igneous_composite['molar_CaO*'] + data_igneous_composite['molar_Na2O'], c=data_igneous_composite['molar_K2O'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_igneous_composite.update_layout({'ternary': {'sum': 100}})
ACNK_igneous_composite.update_ternaries(bgcolor='yellow')
ACNK_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_igneous_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_igneous_composite.show()

# M - F - W compositional space diagram
MFW_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['(M)'], b=data_igneous_composite['(F)'], c=data_igneous_composite['(W)'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_igneous_composite.update_layout({'ternary': {'sum': 100}})
MFW_igneous_composite.update_ternaries(bgcolor='yellow')
MFW_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_igneous_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_igneous_composite.show()


# A - CNK - FM compositional space diagram
ACNKFM_igneous_composite = px.scatter_ternary(data_igneous_composite, a=data_igneous_composite['molar_Al2O3'], b=data_igneous_composite['molar_CaO*'] + data_igneous_composite['molar_Na2O'] + data_igneous_composite['molar_K2O'], c=data_igneous_composite['molar_Fe2O3'] + data_igneous_composite['molar_MgO'], color = data_igneous_composite['subcategory'], symbol = data_igneous_composite['subsubcategory'], hover_name = data_igneous_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_igneous_composite.update_layout({'ternary': {'sum': 100}})
ACNKFM_igneous_composite.update_ternaries(bgcolor='yellow')
ACNKFM_igneous_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_igneous_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_igneous_composite.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_igneous_composite.write_html("igneous_composite plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_igneous_composite, image='svg', filename='igneous_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_igneous_composite, image='jpeg', filename='igneous_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_igneous_composite.write_html("igneous_composite plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_igneous_composite, image='svg', filename='igneous_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_igneous_composite, image='jpeg', filename='igneous_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_igneous_composite.write_html("igneous_composite plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_igneous_composite, image='svg', filename='igneous_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_igneous_composite, image='jpeg', filename='igneous_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('shale_composite') plots

# A - CN - K compositional space diagram
ACNK_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['molar_Al2O3'], b=data_shale_composite['molar_CaO*'] + data_shale_composite['molar_Na2O'], c=data_shale_composite['molar_K2O'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_shale_composite.update_layout({'ternary': {'sum': 100}})
ACNK_shale_composite.update_ternaries(bgcolor='yellow')
ACNK_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_shale_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_shale_composite.show()

# M - F - W compositional space diagram
MFW_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['(M)'], b=data_shale_composite['(F)'], c=data_shale_composite['(W)'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_shale_composite.update_layout({'ternary': {'sum': 100}})
MFW_shale_composite.update_ternaries(bgcolor='yellow')
MFW_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_shale_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_shale_composite.show()


# A - CNK - FM compositional space diagram
ACNKFM_shale_composite = px.scatter_ternary(data_shale_composite, a=data_shale_composite['molar_Al2O3'], b=data_shale_composite['molar_CaO*'] + data_shale_composite['molar_Na2O'] + data_shale_composite['molar_K2O'], c=data_shale_composite['molar_Fe2O3'] + data_shale_composite['molar_MgO'], color = data_shale_composite['subcategory'], symbol = data_shale_composite['subsubcategory'], hover_name = data_shale_composite['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_shale_composite.update_layout({'ternary': {'sum': 100}})
ACNKFM_shale_composite.update_ternaries(bgcolor='yellow')
ACNKFM_shale_composite.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_shale_composite.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_shale_composite.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_shale_composite.write_html("shale_composite plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_shale_composite, image='svg', filename='shale_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_shale_composite, image='jpeg', filename='shale_composite plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_shale_composite.write_html("shale_composite plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_shale_composite, image='svg', filename='shale_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_shale_composite, image='jpeg', filename='shale_composite plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_shale_composite.write_html("shale_composite plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_shale_composite, image='svg', filename='shale_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_shale_composite, image='jpeg', filename='shale_composite plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('UCC', 'river_sediment', 'region') plots

# A - CN - K compositional space diagram
ACNK_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['molar_Al2O3'], b=data_UCC_rs_r['molar_CaO*'] + data_UCC_rs_r['molar_Na2O'], c=data_UCC_rs_r['molar_K2O'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_rs_r.update_ternaries(bgcolor='yellow')
ACNK_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_UCC_rs_r.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_UCC_rs_r.show()

# M - F - W compositional space diagram
MFW_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['(M)'], b=data_UCC_rs_r['(F)'], c=data_UCC_rs_r['(W)'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
MFW_UCC_rs_r.update_ternaries(bgcolor='yellow')
MFW_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_UCC_rs_r.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_UCC_rs_r.show()


# A - CNK - FM compositional space diagram
ACNKFM_UCC_rs_r = px.scatter_ternary(data_UCC_rs_r, a=data_UCC_rs_r['molar_Al2O3'], b=data_UCC_rs_r['molar_CaO*'] + data_UCC_rs_r['molar_Na2O'] + data_UCC_rs_r['molar_K2O'], c=data_UCC_rs_r['molar_Fe2O3'] + data_UCC_rs_r['molar_MgO'], color = data_UCC_rs_r['subcategory'], symbol = data_UCC_rs_r['subsubcategory'], hover_name = data_UCC_rs_r['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_UCC_rs_r.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_rs_r.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_rs_r.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_UCC_rs_r.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_UCC_rs_r.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_rs_r.write_html("UCC_rs_r plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_rs_r, image='svg', filename='UCC_rs_r plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_UCC_rs_r.write_html("UCC_rs_r plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_rs_r, image='svg', filename='UCC_rs_r plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_UCC_rs_r.write_html("UCC_rs_r plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_rs_r, image='svg', filename='UCC_rs_r plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_rs_r, image='jpeg', filename='UCC_rs_r plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('UCC', Manasbal_Lake') plots

# A - CN - K compositional space diagram
ACNK_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['molar_Al2O3'], b=data_UCC_ml['molar_CaO*'] + data_UCC_ml['molar_Na2O'], c=data_UCC_ml['molar_K2O'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_UCC_ml.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_ml.update_ternaries(bgcolor='yellow')
ACNK_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_UCC_ml.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_UCC_ml.show()

# M - F - W compositional space diagram
MFW_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['(M)'], b=data_UCC_ml['(F)'], c=data_UCC_ml['(W)'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_UCC_ml.update_layout({'ternary': {'sum': 100}})
MFW_UCC_ml.update_ternaries(bgcolor='yellow')
MFW_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_UCC_ml.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_UCC_ml.show()


# A - CNK - FM compositional space diagram
ACNKFM_UCC_ml = px.scatter_ternary(data_UCC_ml, a=data_UCC_ml['molar_Al2O3'], b=data_UCC_ml['molar_CaO*'] + data_UCC_ml['molar_Na2O'] + data_UCC_ml['molar_K2O'], c=data_UCC_ml['molar_Fe2O3'] + data_UCC_ml['molar_MgO'], color = data_UCC_ml['subcategory'], symbol = data_UCC_ml['subsubcategory'], hover_name = data_UCC_ml['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_UCC_ml.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_ml.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_ml.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_UCC_ml.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_UCC_ml.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_ml.write_html("UCC_ml plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_ml, image='svg', filename='UCC_ml plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_ml, image='jpeg', filename='UCC_ml plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_UCC_ml.write_html("UCC_ml plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_ml, image='svg', filename='UCC_ml plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_ml, image='jpeg', filename='UCC_ml plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_UCC_ml.write_html("UCC_ml plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_ml, image='svg', filename='UCC_ml plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_ml, image='jpeg', filename='UCC_ml plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('UCC', 'Surma_Group', 'Barail_Group') plots

# A - CN - K compositional space diagram
ACNK_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['molar_Al2O3'], b=data_UCC_sg_bg['molar_CaO*'] + data_UCC_sg_bg['molar_Na2O'], c=data_UCC_sg_bg['molar_K2O'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_sg_bg.update_ternaries(bgcolor='yellow')
ACNK_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_UCC_sg_bg.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_UCC_sg_bg.show()

# M - F - W compositional space diagram
MFW_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['(M)'], b=data_UCC_sg_bg['(F)'], c=data_UCC_sg_bg['(W)'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
MFW_UCC_sg_bg.update_ternaries(bgcolor='yellow')
MFW_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_UCC_sg_bg.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_UCC_sg_bg.show()


# A - CNK - FM compositional space diagram
ACNKFM_UCC_sg_bg = px.scatter_ternary(data_UCC_sg_bg, a=data_UCC_sg_bg['molar_Al2O3'], b=data_UCC_sg_bg['molar_CaO*'] + data_UCC_sg_bg['molar_Na2O'] + data_UCC_sg_bg['molar_K2O'], c=data_UCC_sg_bg['molar_Fe2O3'] + data_UCC_sg_bg['molar_MgO'], color = data_UCC_sg_bg['subcategory'], symbol = data_UCC_sg_bg['sample'], hover_name = data_UCC_sg_bg['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_UCC_sg_bg.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_sg_bg.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_sg_bg.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_UCC_sg_bg.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_UCC_sg_bg.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_sg_bg.write_html("UCC_sg_bg plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_UCC_sg_bg.write_html("UCC_sg_bg plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_UCC_sg_bg.write_html("UCC_sg_bg plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_sg_bg, image='svg', filename='UCC_sg_bg plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_sg_bg, image='jpeg', filename='UCC_sg_bg plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('UCC', channel_sediments', overbank_sediments', suspended_sediments') plots

# A - CN - K compositional space diagram
ACNK_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['molar_Al2O3'], b=data_UCC_cs_os_ss['molar_CaO*'] + data_UCC_cs_os_ss['molar_Na2O'], c=data_UCC_cs_os_ss['molar_K2O'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
ACNK_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_UCC_cs_os_ss.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_UCC_cs_os_ss.show()

# M - F - W compositional space diagram
MFW_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['(M)'], b=data_UCC_cs_os_ss['(F)'], c=data_UCC_cs_os_ss['(W)'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
MFW_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
MFW_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_UCC_cs_os_ss.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_UCC_cs_os_ss.show()


# A - CNK - FM compositional space diagram
ACNKFM_UCC_cs_os_ss = px.scatter_ternary(data_UCC_cs_os_ss, a=data_UCC_cs_os_ss['molar_Al2O3'], b=data_UCC_cs_os_ss['molar_CaO*'] + data_UCC_cs_os_ss['molar_Na2O'] + data_UCC_cs_os_ss['molar_K2O'], c=data_UCC_cs_os_ss['molar_Fe2O3'] + data_UCC_cs_os_ss['molar_MgO'], color = data_UCC_cs_os_ss['subcategory'], symbol = data_UCC_cs_os_ss['sample'], hover_name = data_UCC_cs_os_ss['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_UCC_cs_os_ss.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_cs_os_ss.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_cs_os_ss.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_UCC_cs_os_ss.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_UCC_cs_os_ss.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_UCC_cs_os_ss.write_html("UCC_cs_os_ss plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_cs_os_ss, image='svg', filename='UCC_cs_os_ss plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_cs_os_ss, image='jpeg', filename='UCC_cs_os_ss plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


# Subcategory ('UCC', Silty_clay', 'Sand') plots

# A - CN - K compositional space diagram
ACNK_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['molar_Al2O3'], b=data_UCC_sc_s['molar_CaO*'] + data_UCC_sc_s['molar_Na2O'], c=data_UCC_sc_s['molar_K2O'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNK_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
ACNK_UCC_sc_s.update_ternaries(bgcolor='yellow')
ACNK_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNK_UCC_sc_s.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O'},
            'caxis':{'title': 'K<sub>2</sub>O'}
            }
})

ACNK_UCC_sc_s.show()

# M - F - W compositional space diagram
MFW_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['(M)'], b=data_UCC_sc_s['(F)'], c=data_UCC_sc_s['(W)'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

MFW_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
MFW_UCC_sc_s.update_ternaries(bgcolor='yellow')
MFW_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

MFW_UCC_sc_s.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'M'},
            'baxis':{'title': 'F'},
            'caxis':{'title': 'W'}
            }
})

MFW_UCC_sc_s.show()


# A - CNK - FM compositional space diagram
ACNKFM_UCC_sc_s = px.scatter_ternary(data_UCC_sc_s, a=data_UCC_sc_s['molar_Al2O3'], b=data_UCC_sc_s['molar_CaO*'] + data_UCC_sc_s['molar_Na2O'] + data_UCC_sc_s['molar_K2O'], c=data_UCC_sc_s['molar_Fe2O3'] + data_UCC_sc_s['molar_MgO'], color = data_UCC_sc_s['subcategory'], symbol = data_UCC_sc_s['subsubcategory'], hover_name = data_UCC_sc_s['sample'], color_discrete_sequence=px.colors.qualitative.Antique)

ACNKFM_UCC_sc_s.update_layout({'ternary': {'sum': 100}})
ACNKFM_UCC_sc_s.update_ternaries(bgcolor='yellow')
ACNKFM_UCC_sc_s.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))

ACNKFM_UCC_sc_s.update_layout({
        'ternary':
            {
            'sum':100,
            'aaxis':{'title': 'Al<sub>2</sub>O<sub>3</sub>'},
            'baxis':{'title': 'CaO*+Na<sub>2</sub>O+K<sub>2</sub>O'},
            'caxis':{'title': 'Fe<sub>2</sub>O<sub>3</sub>+MgO'}
            }
})

ACNKFM_UCC_sc_s.show()

# uncomment below lines of code to export the plot to your local machine
# ACNK_UCC_sc_s.write_html("UCC_sc_s plot in A - CN - K compositional space.html") # for 'html' version
# iplot(ACNK_UCC_sc_s, image='svg', filename='UCC_sc_s plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNK_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in A - CN - K compositional space', image_width=800, image_height=800) # for 'jpeg' version

# MFW_UCC_sc_s.write_html("UCC_sc_s plot in M - F - W compositional space.html") # for 'html' version
# iplot(MFW_UCC_sc_s, image='svg', filename='UCC_sc_s plot in M - F - W compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(MFW_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in M - F - W compositional space', image_width=800, image_height=800) # for 'jpeg' version

# ACNKFM_UCC_sc_s.write_html("UCC_sc_s plot in A - CNK - FM compositional space.html") # for 'html' version
# iplot(ACNKFM_UCC_sc_s, image='svg', filename='UCC_sc_s plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'svg' version
# iplot(ACNKFM_UCC_sc_s, image='jpeg', filename='UCC_sc_s plot in A - CNK - FM compositional space', image_width=800, image_height=800) # for 'jpeg' version


data['SiO2/Al2O3'] = data['SiO2'] / data['Al2O3']
data['K2O/Al2O3']  = data['K2O'] / data['Al2O3']
data['Al2O3/TiO2'] = data['Al2O3'] / data['TiO2']

# Normalization of 'M', 'F' and 'W' values to 100
data['(M)'] = 100 * data['(M)'] / (data['(M)'] + data['(F)'] + data['(W)'])
data['(F)'] = 100 * data['(F)'] / (data['(M)'] + data['(F)'] + data['(W)'])
data['(W)'] = 100 - (data['(M)'] + data['(F)'])


data = data.drop(['CO2', 'molar_SiO2', 'molar_TiO2', 'molar_Al2O3', 'molar_Fe2O3', 'molar_MnO', 'molar_MgO', 'molar_CaO',
                  'molar_Na2O', 'molar_K2O', 'molar_P2O5', 'molar_CO2', 'diff', 'molar_CaO*', 'sum', 'SiO2.100', 'TiO2.100',
                  'Al2O3.100', 'Fe2O3.100', 'MgO.100', 'CaO.100', 'Na2O.100', 'K2O.100', '(M)', '(F)'], axis=1)

data = data.round(2)


# exporting csv file of raw data along with calculated weathering indices to your local machine

data.to_csv('data.csv', index = False, float_format='%.2f')


box_CIW = px.box(data, x="subcategory", y="(CIW)", color="subsubcategory")

box_CIW.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='CIW',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_CIW.write_html("Boxplot of CIW variation.html") # for 'html' version
# iplot(box_CIW, image='svg', filename='Boxplot of CIW variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIW, image='jpeg', filename='Boxplot of CIW variation', image_width=800, image_height=800) # for 'jpeg' version


box_CPA = px.box(data, x="subcategory", y="(CPA)", color="subsubcategory")

box_CPA.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='CPA',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_CPA.write_html("Boxplot of CPA variation.html") # for 'html' version
# iplot(box_CPA, image='svg', filename='Boxplot of CPA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CPA, image='jpeg', filename='Boxplot of CPA variation', image_width=800, image_height=800) # for 'jpeg' version


box_CIA = px.box(data, x="subcategory", y="(CIA)", color="subsubcategory")

box_CIA.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='CIA',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_CIA.write_html("Boxplot of CIA variation.html") # for 'html' version
# iplot(box_CIA, image='svg', filename='Boxplot of CIA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIA, image='jpeg', filename='Boxplot of CIA variation', image_width=800, image_height=800) # for 'jpeg' version


box_PIA = px.box(data, x="subcategory", y="(PIA)", color="subsubcategory")

box_PIA.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='PIA',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_PIA.write_html("Boxplot of PIA variation.html") # for 'html' version
# iplot(box_PIA, image='svg', filename='Boxplot of PIA variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_PIA, image='jpeg', filename='Boxplot of PIA variation', image_width=800, image_height=800) # for 'jpeg' version


box_CIX = px.box(data, x="subcategory", y="(CIX)", color="subsubcategory")

box_CIX.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='CIX',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_CIX.write_html("Boxplot of CIX variation.html") # for 'html' version
# iplot(box_CIX, image='svg', filename='Boxplot of CIX variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_CIX, image='jpeg', filename='Boxplot of CIX variation', image_width=800, image_height=800) # for 'jpeg' version


box_ICV = px.box(data, x="subcategory", y="(ICV)", color="subsubcategory")

box_ICV.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='ICV',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_ICV.write_html("Boxplot of ICV variation.html") # for 'html' version
# iplot(box_ICV, image='svg', filename='Boxplot of ICV variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_ICV, image='jpeg', filename='Boxplot of ICV variation', image_width=800, image_height=800) # for 'jpeg' version


box_WIP = px.box(data, x="subcategory", y="(WIP)", color="subsubcategory")

box_WIP.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='WIP',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_WIP.write_html("Boxplot of WIP variation.html") # for 'html' version
# iplot(box_WIP, image='svg', filename='Boxplot of WIP variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_WIP, image='jpeg', filename='Boxplot of WIP variation', image_width=800, image_height=800) # for 'jpeg' version


box_W = px.box(data, x="subcategory", y="(W)", color="subsubcategory")

box_W.update_layout(
    xaxis_title='sample subcategory',
    yaxis_title='W',
    showlegend=False
    )

# uncomment below lines of code to export the plot to your local machine
# box_W.write_html("Boxplot of W variation.html") # for 'html' version
# iplot(box_W, image='svg', filename='Boxplot of W variation', image_width=800, image_height=800) # for 'svg' version
# iplot(box_W, image='jpeg', filename='Boxplot of W variation', image_width=800, image_height=800) # for 'jpeg' version


# filtering data into subcategories ('Manasbal_Lake', 'Surma_Group', 'Barail_Group', 'channel_sediments', 'overbank_sediments', 'suspended_sediments', 'Silty_clay', 'Sand')  
data_ml = data[data.subcategory == 'Manasbal_Lake']
data_sg = data[data.subcategory == 'Surma_Group']
data_bg = data[data.subcategory == 'Barail_Group']
data_cs = data[data.subcategory == 'channel_sediments']
data_os = data[data.subcategory == 'overbank_sediments']
data_ss = data[data.subcategory == 'suspended_sediments']
data_sc = data[data.subcategory == 'Silty_clay']
data_s  = data[data.subcategory == 'Sand']


# scatter matrix of weathering indices of subcategories

# scatter matrix of weathering indices of subsubcategory ('Manasbal_Lake')
scatter_ml = px.scatter_matrix(data_ml, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_ml.show()

# scatter matrix of weathering indices of subsubcategory ('Surma_Group')
scatter_sg = px.scatter_matrix(data_sg, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_sg.show()

# scatter matrix of weathering indices of subsubcategory ('Barail_Group')
scatter_bg = px.scatter_matrix(data_bg, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_bg.show()

# scatter matrix of weathering indices of subsubcategory ('channel_sediments')
scatter_cs = px.scatter_matrix(data_cs, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_cs.show()

# scatter matrix of weathering indices of subsubcategory ('overbank_sediments')
scatter_os = px.scatter_matrix(data_os, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_os.show()

# scatter matrix of weathering indices of subsubcategory ('suspended_sediments')
scatter_ss = px.scatter_matrix(data_ss, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_ss.show()

# scatter matrix of weathering indices of subsubcategory ('Silty_clay')
scatter_sc = px.scatter_matrix(data_sc, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_sc.show()

# scatter matrix of weathering indices of subsubcategory ('Sand')
scatter_s = px.scatter_matrix(data_s, dimensions=["(CIW)", "(CPA)", "(CIA)", "(PIA)", "(CIX)", "(ICV)", "(WIP)", "(W)"], hover_name="sample")
scatter_s.show()

# uncomment below lines of code to export the plot to your local machine
# scatter_ml.write_html("scatter matrix of weathering indices of Manasbal Lake.html") # for 'html' version
# iplot(scatter_ml, image='svg', filename='scatter matrix of weathering indices of Manasbal Lake', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_ml, image='jpeg', filename='scatter matrix of weathering indices of Manasbal Lake', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_sg.write_html("scatter matrix of weathering indices of Surma Group.html") # for 'html' version
# iplot(scatter_sg, image='svg', filename='scatter matrix of weathering indices of Surma Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_sg, image='jpeg', filename='scatter matrix of weathering indices of Surma Group', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_bg.write_html("scatter matrix of weathering indices of Barail Group.html") # for 'html' version
# iplot(scatter_bg, image='svg', filename='scatter matrix of weathering indices of Barail Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_bg, image='jpeg', filename='scatter matrix of weathering indices of Barail Group', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_cs.write_html("scatter matrix of weathering indices of channel_sediments.html") # for 'html' version
# iplot(scatter_cs, image='svg', filename='scatter matrix of weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_cs, image='jpeg', filename='scatter matrix of weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_os.write_html("scatter matrix of weathering indices of overbank_sediments.html") # for 'html' version
# iplot(scatter_os, image='svg', filename='scatter matrix of weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_os, image='jpeg', filename='scatter matrix of weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_ss.write_html("scatter matrix of weathering indices of suspended_sediments.html") # for 'html' version
# iplot(scatter_ss, image='svg', filename='scatter matrix of weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_ss, image='jpeg', filename='scatter matrix of weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_sc.write_html("scatter matrix of weathering indices of Silty_clay.html") # for 'html' version
# iplot(scatter_sc, image='svg', filename='scatter matrix of weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_sc, image='jpeg', filename='scatter matrix of weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'jpeg' version

# scatter_s.write_html("scatter matrix of weathering indices of Sand.html") # for 'html' version
# iplot(scatter_s, image='svg', filename='scatter matrix of weathering indices of Sand', image_width=1500, image_height=600) # for 'svg' version
# iplot(scatter_s, image='jpeg', filename='scatter matrix of weathering indices of Sand', image_width=1500, image_height=600) # for 'jpeg' version


# correlation matrix of subcategories

# correlation matrix of subcategory ('Manasbal_Lake')
corrMatrix_ml   = round(data_ml.corr(), 2)

# correlation matrix of subcategory ('Surma_Group')
corrMatrix_sg   = round(data_sg.corr(), 2)

# correlation matrix of subcategory ('Barail_Group')
corrMatrix_bg   = round(data_bg.corr(), 2)

# correlation matrix of subcategory ('channel_sediments')
corrMatrix_cs   = round(data_cs.corr(), 2)

# correlation matrix of subcategory ('overbank_sediments')
corrMatrix_os   = round(data_os.corr(), 2)

# correlation matrix of subcategory ('suspended_sediments')
corrMatrix_ss   = round(data_ss.corr(), 2)

# correlation matrix of subcategory ('Silty_clay')
corrMatrix_sc   = round(data_sc.corr(), 2)

# correlation matrix of subcategory ('Sand')
corrMatrix_s   = round(data_s.corr(), 2)


# Uncomment the below lines to export the data file to your local machine

# corrMatrix_ml.to_csv('corrMatrix_ml.csv', float_format='%.2f')
# corrMatrix_sg.to_csv('corrMatrix_sg.csv', float_format='%.2f')
# corrMatrix_bg.to_csv('corrMatrix_bg.csv', float_format='%.2f')
# corrMatrix_cs.to_csv('corrMatrix_cs.csv', float_format='%.2f')
# corrMatrix_os.to_csv('corrMatrix_os.csv', float_format='%.2f')
# corrMatrix_ss.to_csv('corrMatrix_ss.csv', float_format='%.2f')
# corrMatrix_sc.to_csv('corrMatrix_sc.csv', float_format='%.2f')
# corrMatrix_s.to_csv('corrMatrix_s.csv', float_format='%.2f')


# Visual representation of correlation matrix of subsubcategory ('Manasbal_Lake')
corrMatrix_ml.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('Surma_Group')
corrMatrix_sg.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('Barail_Group')
corrMatrix_bg.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('channel_sediments')
corrMatrix_cs.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('overbank_sediments')
corrMatrix_os.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('suspended_sediments')
corrMatrix_ss.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('Silty_clay')
corrMatrix_sc.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Visual representation of correlation matrix of subsubcategory ('Sand')
corrMatrix_s.style.background_gradient(cmap='coolwarm', axis=None).set_precision(2)


# Correlation diagram of subcategory ('Manasbal_Lake')

corr_ml = make_subplots(rows=1, cols=8)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_ml.add_trace(go.Scatter(
    x=data_ml['Al2O3/TiO2'],
    y=data_ml['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_ml.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Manasbal_Lake", showlegend=False, hovermode="x")

corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_ml.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_ml.update_yaxes(title_text="CIW", row=1, col=1)
corr_ml.update_yaxes(title_text="CPA", row=1, col=2)
corr_ml.update_yaxes(title_text="CIA", row=1, col=3)
corr_ml.update_yaxes(title_text="PIA", row=1, col=4)
corr_ml.update_yaxes(title_text="CIX", row=1, col=5)
corr_ml.update_yaxes(title_text="ICV", row=1, col=6)
corr_ml.update_yaxes(title_text="WIP", row=1, col=7)
corr_ml.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_ml.write_html("Correlation diagram of chemical weathering indices of Manasbal_Lake.html") # for 'html' version
# iplot(corr_ml, image='svg', filename='Correlation diagram of chemical weathering indices of Manasbal_Lake', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_ml, image='jpeg', filename='Correlation diagram of chemical weathering indices of Manasbal_Lake', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (Surma_Group)

corr_sg = make_subplots(rows=1, cols=8)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_sg.add_trace(go.Scatter(
    x=data_sg['Al2O3/TiO2'],
    y=data_sg['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_sg.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Surma_Group", showlegend=False, hovermode="x")

corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_sg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_sg.update_yaxes(title_text="CIW", row=1, col=1)
corr_sg.update_yaxes(title_text="CPA", row=1, col=2)
corr_sg.update_yaxes(title_text="CIA", row=1, col=3)
corr_sg.update_yaxes(title_text="PIA", row=1, col=4)
corr_sg.update_yaxes(title_text="CIX", row=1, col=5)
corr_sg.update_yaxes(title_text="ICV", row=1, col=6)
corr_sg.update_yaxes(title_text="WIP", row=1, col=7)
corr_sg.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_sg.write_html("Correlation diagram of chemical weathering indices of Surma_Group.html") # for 'html' version
# iplot(corr_sg, image='svg', filename='Correlation diagram of chemical weathering indices of Surma_Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_sg, image='jpeg', filename='Correlation diagram of chemical weathering indices of Surma_Group', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (Barail_Group)

corr_bg = make_subplots(rows=1, cols=8)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_bg.add_trace(go.Scatter(
    x=data_bg['Al2O3/TiO2'],
    y=data_bg['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_bg.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Barail_Group", showlegend=False, hovermode="x")

corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_bg.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_bg.update_yaxes(title_text="CIW", row=1, col=1)
corr_bg.update_yaxes(title_text="CPA", row=1, col=2)
corr_bg.update_yaxes(title_text="CIA", row=1, col=3)
corr_bg.update_yaxes(title_text="PIA", row=1, col=4)
corr_bg.update_yaxes(title_text="CIX", row=1, col=5)
corr_bg.update_yaxes(title_text="ICV", row=1, col=6)
corr_bg.update_yaxes(title_text="WIP", row=1, col=7)
corr_bg.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_bg.write_html("Correlation diagram of chemical weathering indices of Barail_Group.html") # for 'html' version
# iplot(corr_bg, image='svg', filename='Correlation diagram of chemical weathering indices of Barail_Group', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_bg, image='jpeg', filename='Correlation diagram of chemical weathering indices of Barail_Group', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (channel_sediments)

corr_cs = make_subplots(rows=1, cols=8)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_cs.add_trace(go.Scatter(
    x=data_cs['Al2O3/TiO2'],
    y=data_cs['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_cs.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of channel_sediments", showlegend=False, hovermode="x")

corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_cs.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_cs.update_yaxes(title_text="CIW", row=1, col=1)
corr_cs.update_yaxes(title_text="CPA", row=1, col=2)
corr_cs.update_yaxes(title_text="CIA", row=1, col=3)
corr_cs.update_yaxes(title_text="PIA", row=1, col=4)
corr_cs.update_yaxes(title_text="CIX", row=1, col=5)
corr_cs.update_yaxes(title_text="ICV", row=1, col=6)
corr_cs.update_yaxes(title_text="WIP", row=1, col=7)
corr_cs.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_cs.write_html("Correlation diagram of chemical weathering indices of channel_sediments.html") # for 'html' version
# iplot(corr_cs, image='svg', filename='Correlation diagram of chemical weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_cs, image='jpeg', filename='Correlation diagram of chemical weathering indices of channel_sediments', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (overbank_sediments)

corr_os = make_subplots(rows=1, cols=8)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_os.add_trace(go.Scatter(
    x=data_os['Al2O3/TiO2'],
    y=data_os['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_os.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of overbank_sediments", showlegend=False, hovermode="x")

corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_os.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_os.update_yaxes(title_text="CIW", row=1, col=1)
corr_os.update_yaxes(title_text="CPA", row=1, col=2)
corr_os.update_yaxes(title_text="CIA", row=1, col=3)
corr_os.update_yaxes(title_text="PIA", row=1, col=4)
corr_os.update_yaxes(title_text="CIX", row=1, col=5)
corr_os.update_yaxes(title_text="ICV", row=1, col=6)
corr_os.update_yaxes(title_text="WIP", row=1, col=7)
corr_os.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_os.write_html("Correlation diagram of chemical weathering indices of overbank_sediments.html") # for 'html' version
# iplot(corr_os, image='svg', filename='Correlation diagram of chemical weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_os, image='jpeg', filename='Correlation diagram of chemical weathering indices of overbank_sediments', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (suspended_sediments)

corr_ss = make_subplots(rows=1, cols=8)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_ss.add_trace(go.Scatter(
    x=data_ss['Al2O3/TiO2'],
    y=data_ss['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_ss.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of suspended_sediments", showlegend=False, hovermode="x")

corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_ss.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_ss.update_yaxes(title_text="CIW", row=1, col=1)
corr_ss.update_yaxes(title_text="CPA", row=1, col=2)
corr_ss.update_yaxes(title_text="CIA", row=1, col=3)
corr_ss.update_yaxes(title_text="PIA", row=1, col=4)
corr_ss.update_yaxes(title_text="CIX", row=1, col=5)
corr_ss.update_yaxes(title_text="ICV", row=1, col=6)
corr_ss.update_yaxes(title_text="WIP", row=1, col=7)
corr_ss.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_ss.write_html("Correlation diagram of chemical weathering indices of suspended_sediments.html") # for 'html' version
# iplot(corr_ss, image='svg', filename='Correlation diagram of chemical weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_ss, image='jpeg', filename='Correlation diagram of chemical weathering indices of suspended_sediments', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (Silty_clay)

corr_sc = make_subplots(rows=1, cols=8)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_sc.add_trace(go.Scatter(
    x=data_sc['Al2O3/TiO2'],
    y=data_sc['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_sc.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Silty_clay", showlegend=False, hovermode="x")

corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_sc.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_sc.update_yaxes(title_text="CIW", row=1, col=1)
corr_sc.update_yaxes(title_text="CPA", row=1, col=2)
corr_sc.update_yaxes(title_text="CIA", row=1, col=3)
corr_sc.update_yaxes(title_text="PIA", row=1, col=4)
corr_sc.update_yaxes(title_text="CIX", row=1, col=5)
corr_sc.update_yaxes(title_text="ICV", row=1, col=6)
corr_sc.update_yaxes(title_text="WIP", row=1, col=7)
corr_sc.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_sc.write_html("Correlation diagram of chemical weathering indices of Silty_clay.html") # for 'html' version
# iplot(corr_sc, image='svg', filename='Correlation diagram of chemical weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_sc, image='jpeg', filename='Correlation diagram of chemical weathering indices of Silty_clay', image_width=1500, image_height=600) # for 'jpeg' version


# Correlation diagram of subcategory (Sand)

corr_s = make_subplots(rows=1, cols=8)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(CIW)'],
    mode = 'markers'
), row=1, col=1)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(CPA)'],
    mode = 'markers'
), row=1, col=2)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(CIA)'],
    mode = 'markers'
), row=1, col=3)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(PIA)'],    
    mode = 'markers'
), row=1, col=4)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(CIX)'],    
    mode = 'markers'
), row=1, col=5)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(ICV)'],    
    mode = 'markers'
), row=1, col=6)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(WIP)'],    
    mode = 'markers'
), row=1, col=7)

corr_s.add_trace(go.Scatter(
    x=data_s['Al2O3/TiO2'],
    y=data_s['(W)'],    
    mode = 'markers'
), row=1, col=8)

corr_s.update_layout(height=600, width=1500, title_text="Chemical weathering indices against Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub> of Sand", showlegend=False, hovermode="x")

corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=1)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=2)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=3)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=4)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=5)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=6)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=7)
corr_s.update_xaxes(title_text="Al<sub>2</sub>O<sub>3</sub>/TiO<sub>2</sub>", row=1, col=8)

corr_s.update_yaxes(title_text="CIW", row=1, col=1)
corr_s.update_yaxes(title_text="CPA", row=1, col=2)
corr_s.update_yaxes(title_text="CIA", row=1, col=3)
corr_s.update_yaxes(title_text="PIA", row=1, col=4)
corr_s.update_yaxes(title_text="CIX", row=1, col=5)
corr_s.update_yaxes(title_text="ICV", row=1, col=6)
corr_s.update_yaxes(title_text="WIP", row=1, col=7)
corr_s.update_yaxes(title_text="W",   row=1, col=8)

# uncomment below lines of code to export the plot to your local machine
# corr_s.write_html("Correlation diagram of chemical weathering indices of Sand.html") # for 'html' version
# iplot(corr_s, image='svg', filename='Correlation diagram of chemical weathering indices of Sand', image_width=1500, image_height=600) # for 'svg' version
# iplot(corr_s, image='jpeg', filename='Correlation diagram of chemical weathering indices of Sand', image_width=1500, image_height=600) # for 'jpeg' version


app = dash.Dash()


# function for the table genereation
# user may change the argument ('max_rows') for the desired number of observation/rows (currently set to 10)
def generate_table(dataframe, max_rows=10):
    return html.Table([
        html.Thead(
            html.Tr([html.Th(col) for col in dataframe.columns])
        ),
        html.Tbody([
            html.Tr([
                html.Td(dataframe.iloc[i][col]) for col in dataframe.columns
            ]) for i in range(min(len(dataframe), max_rows))
        ])
    ])


app.layout = html.Div(children=[
    html.H1(
        children='SedWeather',
        style={
            'textAlign': 'center'
        }
    ),

    html.H2(children='A web application framework to visualize Chemical Weathering of Clastic Sediments', 
             style={
        'textAlign': 'center'
    }),
    
    html.H3(children='Major oxides with chemical weathering proxies'),
    
    generate_table(data),
         
    html.H3(children='Boxplot of weathering proxies'),    
        
    html.Div([
    dcc.Tabs([
        dcc.Tab(label='CIW variation', children=[
            dcc.Graph(figure=box_CIW)
        ]),
        
        dcc.Tab(label='CPA variation', children=[
            dcc.Graph(figure=box_CPA)
        ]),
        
        dcc.Tab(label='CIA variation', children=[
            dcc.Graph(figure=box_CIA)
        ]),
        
        dcc.Tab(label='PIA variation', children=[
            dcc.Graph(figure=box_PIA)
        ]),
        
        dcc.Tab(label='CIX variation', children=[
            dcc.Graph(figure=box_CIX)
        ]),
        
        dcc.Tab(label='ICV variation', children=[
            dcc.Graph(figure=box_ICV)
        ]),
        
        dcc.Tab(label='WIP variation', children=[
            dcc.Graph(figure=box_WIP)
        ]),
        
        dcc.Tab(label='W variation', children=[
            dcc.Graph(figure=box_W)
        ])
    ]),
        
              
        html.H3(children='scatter matrix of weathering indices'),
    
        html.Div([
        dcc.Tabs([
        dcc.Tab(label='Manasbal lake', children=[
            dcc.Graph(figure=scatter_ml)
        ]),
        
        dcc.Tab(label='Surma Group', children=[
            dcc.Graph(figure=scatter_sg)
        ]),
        
        dcc.Tab(label='Barail Group', children=[
            dcc.Graph(figure=scatter_bg)
        ]),
        
        dcc.Tab(label='channel sediments', children=[
            dcc.Graph(figure=scatter_cs)
        ]),
        
        dcc.Tab(label='overbank sediments', children=[
            dcc.Graph(figure=scatter_os)
        ]),
        
        dcc.Tab(label='suspended sediments', children=[
            dcc.Graph(figure=scatter_ss)
        ]),
        
        dcc.Tab(label='Silty clay', children=[
            dcc.Graph(figure=scatter_sc)
        ]),
        
        dcc.Tab(label='Sand', children=[
            dcc.Graph(figure=scatter_s)
        ])
    ]),            
            
    html.H3(children='Correlation diagrams'),
    
    html.Div([
    dcc.Tabs([
        dcc.Tab(label='Manasbal Lake', children=[
            dcc.Graph(figure=corr_ml)
        ]),
        
        dcc.Tab(label='Surma Group', children=[
            dcc.Graph(figure=corr_sg)
        ]),
        
        dcc.Tab(label='Barail Group', children=[
            dcc.Graph(figure=corr_bg)
        ]),
        
        dcc.Tab(label='channel sediments', children=[
            dcc.Graph(figure=corr_cs)
        ]),
        
        dcc.Tab(label='overbank sediments', children=[
            dcc.Graph(figure=corr_os)
        ]),
        
        dcc.Tab(label='suspended sediments', children=[
            dcc.Graph(figure=corr_ss)
        ]),
        
        dcc.Tab(label='Silty clay', children=[
            dcc.Graph(figure=corr_sc)
        ]),
        
        dcc.Tab(label='Sand', children=[
            dcc.Graph(figure=corr_s)
        ])
    ]),
        
    
    html.H3(children='user-defined variation plot'),   
        html.Div([
        dcc.Graph(id='graph'),
        html.Label([
        "oxide/weathering index",
        dcc.Dropdown(
            id='column-dropdown', clearable=False,
            value = data.columns[0], options=[
                {'label': c, 'value': c}
                for c in data.columns
            ])
        ]),
    ]),
        
        
    html.H3(children='Compositional space diagrams'),
        
    html.H4(children='A-CN-K Compositional space diagram'),
        
    html.H5(children='category and subcategory plots'),
        
        html.Div([
        dcc.Tabs([
        dcc.Tab(label='category plot', children=[
            dcc.Graph(figure=ACNK_cat)
        ]),
        
        dcc.Tab(label='subcategory plot', children=[
            dcc.Graph(figure=ACNK_subcat)
        ])
            
    ]),   
            
            
    
    html.H5(children='user-defined plots'),

        html.Div([
        dcc.Tabs([
        dcc.Tab(label='upper_continental_crust', children=[
            dcc.Graph(figure=ACNK_upper_continental_crust)
        ]),
        
        dcc.Tab(label='igneous_composite', children=[
            dcc.Graph(figure=ACNK_igneous_composite)
        ]),        
        
        dcc.Tab(label='shale_composite', children=[
            dcc.Graph(figure=ACNK_shale_composite)
        ]),
        
        dcc.Tab(label='UCC_rs_r', children=[
            dcc.Graph(figure=ACNK_UCC_rs_r)
        ]),
                    
        dcc.Tab(label='UCC_ml', children=[
            dcc.Graph(figure=ACNK_UCC_ml)
        ]),
                    
        dcc.Tab(label='UCC_sg_bg', children=[
            dcc.Graph(figure=ACNK_UCC_sg_bg)
        ]),
                    
        dcc.Tab(label='UCC_cs_os_ss', children=[
            dcc.Graph(figure=ACNK_UCC_cs_os_ss)
        ]),
                    
        dcc.Tab(label='UCC_sc_s', children=[
            dcc.Graph(figure=ACNK_UCC_sc_s)
        ])
            
    ]),   
            
            
    html.H4(children='M-F-W Compositional space diagram'),
        
    html.H5(children='category and subcategory plots'),
        
        html.Div([
        dcc.Tabs([
        dcc.Tab(label='category plot', children=[
            dcc.Graph(figure=MFW_cat)
        ]),
        
        dcc.Tab(label='subcategory plot', children=[
            dcc.Graph(figure=MFW_subcat)
        ])
            
    ]),   
            
            
    
    html.H5(children='user-defined plots'),

        html.Div([
        dcc.Tabs([
        dcc.Tab(label='upper_continental_crust', children=[
            dcc.Graph(figure=MFW_upper_continental_crust)
        ]),
        
        dcc.Tab(label='igneous_composite', children=[
            dcc.Graph(figure=MFW_igneous_composite)
        ]),        
        
        dcc.Tab(label='shale_composite', children=[
            dcc.Graph(figure=MFW_shale_composite)
        ]),
        
        dcc.Tab(label='UCC_rs_r', children=[
            dcc.Graph(figure=MFW_UCC_rs_r)
        ]),
                    
        dcc.Tab(label='UCC_ml', children=[
            dcc.Graph(figure=MFW_UCC_ml)
        ]),
                    
        dcc.Tab(label='UCC_sg_bg', children=[
            dcc.Graph(figure=MFW_UCC_sg_bg)
        ]),
                    
        dcc.Tab(label='UCC_cs_os_ss', children=[
            dcc.Graph(figure=MFW_UCC_cs_os_ss)
        ]),
                    
        dcc.Tab(label='UCC_sc_s', children=[
            dcc.Graph(figure=MFW_UCC_sc_s)
        ])
            
    ]),
       
            
            
    html.H4(children='A-CNK-FM Compositional space diagram'),
        
    html.H5(children='category and subcategory plots'),
        
        html.Div([
        dcc.Tabs([
        dcc.Tab(label='category plot', children=[
            dcc.Graph(figure=ACNKFM_cat)
        ]),
        
        dcc.Tab(label='subcategory plot', children=[
            dcc.Graph(figure=ACNKFM_subcat)
        ])
            
    ]),   
            
            
    
    html.H5(children='user-defined plots'),

        html.Div([
        dcc.Tabs([
        dcc.Tab(label='upper_continental_crust', children=[
            dcc.Graph(figure=ACNKFM_upper_continental_crust)
        ]),
        
        dcc.Tab(label='igneous_composite', children=[
            dcc.Graph(figure=ACNKFM_igneous_composite)
        ]),        
        
        dcc.Tab(label='shale_composite', children=[
            dcc.Graph(figure=ACNKFM_shale_composite)
        ]),
        
        dcc.Tab(label='UCC_rs_r', children=[
            dcc.Graph(figure=ACNKFM_UCC_rs_r)
        ]),
                    
        dcc.Tab(label='UCC_ml', children=[
            dcc.Graph(figure=ACNKFM_UCC_ml)
        ]),
                    
        dcc.Tab(label='UCC_sg_bg', children=[
            dcc.Graph(figure=ACNKFM_UCC_sg_bg)
        ]),
                    
        dcc.Tab(label='UCC_cs_os_ss', children=[
            dcc.Graph(figure=ACNKFM_UCC_cs_os_ss)
        ]),
                    
        dcc.Tab(label='UCC_sc_s', children=[
            dcc.Graph(figure=ACNKFM_UCC_sc_s)
        ])
            
    ])
            
        
])          
])                
])
])  
])  
])     
]) 
])     
])
])


# Define callback to update graph
@app.callback(
    Output('graph', 'figure'),
    [Input("column-dropdown", "value")]
)
def update_figure(column):
    return px.scatter(
        data, x="sample", y=column, hover_name="sample",
        color="subcategory", symbol="subsubcategory",
        render_mode="webgl", title="Variation Plot",
        height=600, width=1500, color_discrete_sequence=px.colors.qualitative.Antique
    )


if __name__ == '__main__':
    app.run_server(port = 4050)

Dash is running on http://127.0.0.1:4050/

 * Serving Flask app "__main__" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: off

 * Running on http://127.0.0.1:4050/ (Press CTRL+C to quit)
127.0.0.1 - - [01/Nov/2021 12:37:20] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:20] "GET /_dash-dependencies HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_favicon.ico?v=1.21.0 HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-layout HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-graph.js HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-dropdown.js HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "POST /_dash-update-component HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:21] "GET /_dash-component-suites/dash_core_components/async-plotlyjs.js HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:37:47] "POST /_dash-update-component HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:39:00] "POST /_dash-update-component HTTP/1.1" 200 -
127.0.0.1 - - [01/Nov/2021 12:39:11] "POST /_dash-update-component HTTP/1.1" 200 -

Sample Name/ID	Sample Category/Subcategory	Reference
UCC-1	Upper Continental Crust	Shaw et al., 1967, 1976, 1986
UCC-2	Upper Continental Crust	Fahrig and Eade, 1968; Eade and Fahrig, 1973
UCC-3	Upper Continental Crust	Ronov and Yaroshevskiy, 1976
UCC-4	Upper Continental Crust	Gao et al., 1998
UCC-5	Upper Continental Crust	Taylor and McLennan, 1985, 1995
UCC-6	Upper Continental Crust	Wedepohl, 1995
UCC-7	Upper Continental Crust	Rudnick and Gao, 2003
UCC-8	Upper Continental Crust	Togashi et al., 2000
Dunite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Harzburgite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Peridotite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Lherzolite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Anorthosite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Gabbro	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Basalt	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Tholeiite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Andesite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Dacite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Tonalite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Diorite and Granodiorite quartz diorite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Granodiorite and quartz monzodiorite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Rhyolite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Granite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Trachyte	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Syenite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Nepheline syenite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
Phonolite	Igneous Composite	Hyndman, 1985; Le Maitre, 1976
NASC	Shale Composite	McLennan et al., 1983; McLennan, 2009; Gromet et al., 1984
PAAS	Shale Composite	Taylor and McLennan, 1985; Barth et al., 2000; Haskin and Haskin, 1966; McLennan, 2009; Nance and Taylor, 1976; McLennan, 1981, 1989
shale	Shale Composite	Mason, 1982
Amazon	Suspended Sediment	McLennan, 1993
Colorado	Suspended Sediment	McLennan, 1993
Columbia	Suspended Sediment	McLennan, 1993
Congo (Zaire)	Suspended Sediment	McLennan, 1993
Danube	Suspended Sediment	McLennan, 1993
Gangess/Brahmaputra	Suspended Sediment	McLennan, 1993
Huanghe	Suspended Sediment	McLennan, 1993
Indus	Suspended Sediment	McLennan, 1993
Mackenzie	Suspended Sediment	McLennan, 1993
Mekong	Suspended Sediment	McLennan, 1993
Mississippi	Suspended Sediment	McLennan, 1993
Niger	Suspended Sediment	McLennan, 1993
Nile	Suspended Sediment	McLennan, 1993
Orinoco	Suspended Sediment	McLennan, 1993
Parana	Suspended Sediment	McLennan, 1993
St. Lawrence	Suspended Sediment	McLennan, 1993
Western Europe	Denudation Region	McLennan, 1993
Alpine Europe	Denudation Region	McLennan, 1993
Central America (incl. Mexico)	Denudation Region	McLennan, 1993
Northwest South America	Denudation Region	McLennan, 1993
Ocean Islands (excl. New Zealand)	Denudation Region	McLennan, 1993
New Zealand (South Island)	Denudation Region	McLennan, 1993
U.S. Atlantic Coast	Denudation Region	McLennan, 1993

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5	(CIW)	(CPA)	(CIA)	(PIA)	(CIX)	(ICV)	(WIP)	(W)	SiO2/Al2O3	K2O/Al2O3	Al2O3/TiO2
SiO2	1.00	-0.02	0.35	0.19	-0.19	0.08	-0.68	0.58	-0.11	0.55	-0.10	0.34	0.33	0.32	0.31	0.30	-0.42	-0.73	0.56	-0.16	0.25	0.36
TiO2	-0.02	1.00	0.17	0.29	0.38	-0.04	0.00	-0.16	-0.06	-0.02	-0.08	0.19	0.19	0.21	0.22	0.23	-0.08	-0.02	0.01	-0.20	-0.30	-0.10
Al2O3	0.35	0.17	1.00	0.47	0.41	0.79	-0.55	0.66	0.23	0.85	0.32	0.72	0.73	0.74	0.75	0.76	-0.92	-0.40	0.77	-0.95	-0.31	0.96
Fe2O3	0.19	0.29	0.47	1.00	0.77	0.40	-0.35	0.42	-0.09	0.48	0.09	0.41	0.39	0.41	0.42	0.40	-0.35	-0.37	0.11	-0.37	-0.06	0.38
FeO	-0.19	0.38	0.41	0.77	1.00	0.38	0.03	0.19	0.00	0.28	0.13	0.37	0.37	0.38	0.40	0.39	-0.28	0.07	-0.06	-0.46	-0.22	0.31
MnO	0.08	-0.04	0.79	0.40	0.38	1.00	-0.44	0.64	0.19	0.78	0.56	0.59	0.61	0.60	0.59	0.61	-0.78	-0.30	0.55	-0.82	0.01	0.81
MgO	-0.68	0.00	-0.55	-0.35	0.03	-0.44	1.00	-0.68	-0.26	-0.64	-0.33	-0.25	-0.25	-0.25	-0.24	-0.24	0.72	0.91	-0.71	0.42	-0.11	-0.55
CaO	0.58	-0.16	0.66	0.42	0.19	0.64	-0.68	1.00	0.25	0.75	0.36	0.40	0.40	0.39	0.39	0.39	-0.65	-0.55	0.46	-0.52	0.13	0.71
Na2O	-0.11	-0.06	0.23	-0.09	0.00	0.19	-0.26	0.25	1.00	-0.07	0.63	-0.49	-0.48	-0.46	-0.45	-0.43	-0.17	0.15	0.12	-0.15	-0.46	0.25
K2O	0.55	-0.02	0.85	0.48	0.28	0.78	-0.64	0.75	-0.07	1.00	0.21	0.80	0.80	0.79	0.77	0.78	-0.87	-0.61	0.80	-0.80	0.23	0.87
P2O5	-0.10	-0.08	0.32	0.09	0.13	0.56	-0.33	0.36	0.63	0.21	1.00	-0.12	-0.11	-0.12	-0.12	-0.09	-0.32	-0.06	0.23	-0.30	-0.09	0.35
(CIW)	0.34	0.19	0.72	0.41	0.37	0.59	-0.25	0.40	-0.49	0.80	-0.12	1.00	1.00	1.00	1.00	0.99	-0.70	-0.40	0.59	-0.76	0.07	0.68
(CPA)	0.33	0.19	0.73	0.39	0.37	0.61	-0.25	0.40	-0.48	0.80	-0.11	1.00	1.00	1.00	0.99	0.99	-0.71	-0.39	0.60	-0.77	0.08	0.69
(CIA)	0.32	0.21	0.74	0.41	0.38	0.60	-0.25	0.39	-0.46	0.79	-0.12	1.00	1.00	1.00	1.00	1.00	-0.72	-0.38	0.59	-0.78	0.02	0.70
(PIA)	0.31	0.22	0.75	0.42	0.40	0.59	-0.24	0.39	-0.45	0.77	-0.12	1.00	0.99	1.00	1.00	1.00	-0.71	-0.37	0.59	-0.79	-0.03	0.70
(CIX)	0.30	0.23	0.76	0.40	0.39	0.61	-0.24	0.39	-0.43	0.78	-0.09	0.99	0.99	1.00	1.00	1.00	-0.73	-0.36	0.60	-0.81	-0.02	0.71
(ICV)	-0.42	-0.08	-0.92	-0.35	-0.28	-0.78	0.72	-0.65	-0.17	-0.87	-0.32	-0.70	-0.71	-0.72	-0.71	-0.73	1.00	0.61	-0.86	0.92	0.11	-0.91
(WIP)	-0.73	-0.02	-0.40	-0.37	0.07	-0.30	0.91	-0.55	0.15	-0.61	-0.06	-0.40	-0.39	-0.38	-0.37	-0.36	0.61	1.00	-0.63	0.29	-0.30	-0.39
(W)	0.56	0.01	0.77	0.11	-0.06	0.55	-0.71	0.46	0.12	0.80	0.23	0.59	0.60	0.59	0.59	0.60	-0.86	-0.63	1.00	-0.72	0.01	0.78
SiO2/Al2O3	-0.16	-0.20	-0.95	-0.37	-0.46	-0.82	0.42	-0.52	-0.15	-0.80	-0.30	-0.76	-0.77	-0.78	-0.79	-0.81	0.92	0.29	-0.72	1.00	0.28	-0.91
K2O/Al2O3	0.25	-0.30	-0.31	-0.06	-0.22	0.01	-0.11	0.13	-0.46	0.23	-0.09	0.07	0.08	0.02	-0.03	-0.02	0.11	-0.30	0.01	0.28	1.00	-0.22
Al2O3/TiO2	0.36	-0.10	0.96	0.38	0.31	0.81	-0.55	0.71	0.25	0.87	0.35	0.68	0.69	0.70	0.70	0.71	-0.91	-0.39	0.78	-0.91	-0.22	1.00

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5	(CIW)	(CPA)	(CIA)	(PIA)	(CIX)	(ICV)	(WIP)	(W)	SiO2/Al2O3	K2O/Al2O3	Al2O3/TiO2
SiO2	1.00	-0.96	-0.82	-0.42	-0.72	-0.65	-0.68	0.65	-0.90	-0.51	-0.73	0.59	0.58	0.56	0.53	0.52	-0.48	-0.98	0.66	0.93	0.40	0.64
TiO2	-0.96	1.00	0.88	0.30	0.62	0.68	0.49	-0.53	0.95	0.45	0.61	-0.58	-0.57	-0.54	-0.49	-0.47	0.39	0.94	-0.47	-0.95	-0.51	-0.62
Al2O3	-0.82	0.88	1.00	0.27	0.56	0.43	0.20	-0.56	0.90	0.50	0.65	-0.27	-0.26	-0.20	-0.14	-0.12	-0.06	0.80	-0.18	-0.95	-0.54	-0.19
Fe2O3	-0.42	0.30	0.27	1.00	0.90	0.09	0.47	-0.69	0.08	0.61	0.38	0.30	0.30	0.28	0.27	0.26	0.10	0.28	-0.62	-0.32	0.25	-0.18
FeO	-0.72	0.62	0.56	0.90	1.00	0.32	0.63	-0.72	0.42	0.63	0.59	0.02	0.03	0.04	0.04	0.05	0.22	0.60	-0.70	-0.65	-0.05	-0.34
MnO	-0.65	0.68	0.43	0.09	0.32	1.00	0.42	0.02	0.59	0.05	0.24	-0.63	-0.62	-0.59	-0.55	-0.53	0.68	0.61	-0.51	-0.61	-0.52	-0.73
MgO	-0.68	0.49	0.20	0.47	0.63	0.42	1.00	-0.53	0.39	0.28	0.67	-0.50	-0.51	-0.52	-0.53	-0.54	0.73	0.68	-0.95	-0.44	-0.03	-0.64
CaO	0.65	-0.53	-0.56	-0.69	-0.72	0.02	-0.53	1.00	-0.48	-0.79	-0.75	0.02	0.03	0.05	0.06	0.08	-0.02	-0.62	0.51	0.56	-0.21	0.15
Na2O	-0.90	0.95	0.90	0.08	0.42	0.59	0.39	-0.48	1.00	0.33	0.65	-0.66	-0.65	-0.61	-0.55	-0.54	0.29	0.93	-0.33	-0.93	-0.60	-0.48
K2O	-0.51	0.45	0.50	0.61	0.63	0.05	0.28	-0.79	0.33	1.00	0.37	0.16	0.16	0.12	0.07	0.07	-0.11	0.44	-0.25	-0.50	0.43	-0.11
P2O5	-0.73	0.61	0.65	0.38	0.59	0.24	0.67	-0.75	0.65	0.37	1.00	-0.30	-0.30	-0.26	-0.23	-0.22	0.16	0.77	-0.60	-0.71	-0.32	-0.15
(CIW)	0.59	-0.58	-0.27	0.30	0.02	-0.63	-0.50	0.02	-0.66	0.16	-0.30	1.00	1.00	0.99	0.97	0.97	-0.77	-0.67	0.42	0.42	0.43	0.76
(CPA)	0.58	-0.57	-0.26	0.30	0.03	-0.62	-0.51	0.03	-0.65	0.16	-0.30	1.00	1.00	0.99	0.97	0.97	-0.77	-0.67	0.43	0.41	0.42	0.76
(CIA)	0.56	-0.54	-0.20	0.28	0.04	-0.59	-0.52	0.05	-0.61	0.12	-0.26	0.99	0.99	1.00	0.99	0.99	-0.79	-0.64	0.44	0.37	0.32	0.78
(PIA)	0.53	-0.49	-0.14	0.27	0.04	-0.55	-0.53	0.06	-0.55	0.07	-0.23	0.97	0.97	0.99	1.00	1.00	-0.81	-0.61	0.45	0.31	0.21	0.79
(CIX)	0.52	-0.47	-0.12	0.26	0.05	-0.53	-0.54	0.08	-0.54	0.07	-0.22	0.97	0.97	0.99	1.00	1.00	-0.81	-0.60	0.46	0.29	0.19	0.79
(ICV)	-0.48	0.39	-0.06	0.10	0.22	0.68	0.73	-0.02	0.29	-0.11	0.16	-0.77	-0.77	-0.79	-0.81	-0.81	1.00	0.48	-0.75	-0.19	-0.12	-0.92
(WIP)	-0.98	0.94	0.80	0.28	0.60	0.61	0.68	-0.62	0.93	0.44	0.77	-0.67	-0.67	-0.64	-0.61	-0.60	0.48	1.00	-0.62	-0.91	-0.43	-0.61
(W)	0.66	-0.47	-0.18	-0.62	-0.70	-0.51	-0.95	0.51	-0.33	-0.25	-0.60	0.42	0.43	0.44	0.45	0.46	-0.75	-0.62	1.00	0.41	0.04	0.66
SiO2/Al2O3	0.93	-0.95	-0.95	-0.32	-0.65	-0.61	-0.44	0.56	-0.93	-0.50	-0.71	0.42	0.41	0.37	0.31	0.29	-0.19	-0.91	0.41	1.00	0.54	0.39
K2O/Al2O3	0.40	-0.51	-0.54	0.25	-0.05	-0.52	-0.03	-0.21	-0.60	0.43	-0.32	0.43	0.42	0.32	0.21	0.19	-0.12	-0.43	0.04	0.54	1.00	0.16
Al2O3/TiO2	0.64	-0.62	-0.19	-0.18	-0.34	-0.73	-0.64	0.15	-0.48	-0.11	-0.15	0.76	0.76	0.78	0.79	0.79	-0.92	-0.61	0.66	0.39	0.16	1.00

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5	(CIW)	(CPA)	(CIA)	(PIA)	(CIX)	(ICV)	(WIP)	(W)	SiO2/Al2O3	K2O/Al2O3	Al2O3/TiO2
SiO2	1.00	-0.90	-0.61	-0.49	-0.68	-0.89	-0.75	-0.71	-0.88	0.05	-0.21	0.64	0.63	0.61	0.60	0.56	-0.58	-0.89	0.82	0.75	0.31	0.60
TiO2	-0.90	1.00	0.39	0.25	0.45	0.64	0.73	0.56	0.85	0.22	0.48	-0.73	-0.72	-0.74	-0.73	-0.73	0.77	0.92	-0.69	-0.55	0.05	-0.84
Al2O3	-0.61	0.39	1.00	0.56	0.68	0.71	0.15	0.26	0.29	0.12	0.26	0.13	0.15	0.16	0.17	0.22	-0.26	0.30	-0.27	-0.97	-0.37	0.16
Fe2O3	-0.49	0.25	0.56	1.00	0.96	0.51	0.33	0.34	0.14	-0.20	-0.03	0.16	0.14	0.20	0.22	0.23	-0.03	0.14	-0.50	-0.57	-0.41	0.11
FeO	-0.68	0.45	0.68	0.96	1.00	0.69	0.50	0.39	0.36	-0.21	0.00	-0.02	-0.03	0.03	0.05	0.08	0.09	0.35	-0.65	-0.70	-0.48	-0.04
MnO	-0.89	0.64	0.71	0.51	0.69	1.00	0.64	0.64	0.82	-0.28	-0.10	-0.51	-0.50	-0.45	-0.43	-0.37	0.31	0.75	-0.81	-0.78	-0.56	-0.29
MgO	-0.75	0.73	0.15	0.33	0.50	0.64	1.00	0.39	0.81	-0.25	-0.07	-0.74	-0.75	-0.71	-0.70	-0.68	0.76	0.83	-0.93	-0.25	-0.23	-0.66
CaO	-0.71	0.56	0.26	0.34	0.39	0.64	0.39	1.00	0.67	-0.11	0.04	-0.57	-0.57	-0.54	-0.53	-0.52	0.51	0.62	-0.55	-0.46	-0.21	-0.46
Na2O	-0.88	0.85	0.29	0.14	0.36	0.82	0.81	0.67	1.00	-0.17	0.03	-0.91	-0.90	-0.88	-0.87	-0.83	0.76	0.97	-0.85	-0.44	-0.25	-0.75
K2O	0.05	0.22	0.12	-0.20	-0.21	-0.28	-0.25	-0.11	-0.17	1.00	0.95	0.14	0.15	0.03	0.01	-0.06	0.03	0.05	0.44	-0.14	0.87	-0.19
P2O5	-0.21	0.48	0.26	-0.03	0.00	-0.10	-0.07	0.04	0.03	0.95	1.00	0.01	0.01	-0.09	-0.11	-0.17	0.19	0.24	0.23	-0.32	0.76	-0.37
(CIW)	0.64	-0.73	0.13	0.16	-0.02	-0.51	-0.74	-0.57	-0.91	0.14	0.01	1.00	1.00	0.99	0.99	0.97	-0.91	-0.88	0.72	0.04	0.03	0.87
(CPA)	0.63	-0.72	0.15	0.14	-0.03	-0.50	-0.75	-0.57	-0.90	0.15	0.01	1.00	1.00	0.99	0.99	0.97	-0.92	-0.88	0.73	0.02	0.02	0.88
(CIA)	0.61	-0.74	0.16	0.20	0.03	-0.45	-0.71	-0.54	-0.88	0.03	-0.09	0.99	0.99	1.00	1.00	0.99	-0.93	-0.88	0.66	0.01	-0.09	0.90
(PIA)	0.60	-0.73	0.17	0.22	0.05	-0.43	-0.70	-0.53	-0.87	0.01	-0.11	0.99	0.99	1.00	1.00	1.00	-0.92	-0.87	0.64	0.00	-0.12	0.90
(CIX)	0.56	-0.73	0.22	0.23	0.08	-0.37	-0.68	-0.52	-0.83	-0.06	-0.17	0.97	0.97	0.99	1.00	1.00	-0.94	-0.85	0.60	-0.04	-0.21	0.93
(ICV)	-0.58	0.77	-0.26	-0.03	0.09	0.31	0.76	0.51	0.76	0.03	0.19	-0.91	-0.92	-0.93	-0.92	-0.94	1.00	0.81	-0.66	0.07	0.20	-0.96
(WIP)	-0.89	0.92	0.30	0.14	0.35	0.75	0.83	0.62	0.97	0.05	0.24	-0.88	-0.88	-0.88	-0.87	-0.85	0.81	1.00	-0.80	-0.46	-0.05	-0.81
(W)	0.82	-0.69	-0.27	-0.50	-0.65	-0.81	-0.93	-0.55	-0.85	0.44	0.23	0.72	0.73	0.66	0.64	0.60	-0.66	-0.80	1.00	0.37	0.47	0.55
SiO2/Al2O3	0.75	-0.55	-0.97	-0.57	-0.70	-0.78	-0.25	-0.46	-0.44	-0.14	-0.32	0.04	0.02	0.01	0.00	-0.04	0.07	-0.46	0.37	1.00	0.33	0.03
K2O/Al2O3	0.31	0.05	-0.37	-0.41	-0.48	-0.56	-0.23	-0.21	-0.25	0.87	0.76	0.03	0.02	-0.09	-0.12	-0.21	0.20	-0.05	0.47	0.33	1.00	-0.28
Al2O3/TiO2	0.60	-0.84	0.16	0.11	-0.04	-0.29	-0.66	-0.46	-0.75	-0.19	-0.37	0.87	0.88	0.90	0.90	0.93	-0.96	-0.81	0.55	0.03	-0.28	1.00

	SiO2	TiO2	Al2O3	Fe2O3	FeO	MnO	MgO	CaO	Na2O	K2O	P2O5	(CIW)	(CPA)	(CIA)	(PIA)	(CIX)	(ICV)	(WIP)	(W)	SiO2/Al2O3	K2O/Al2O3	Al2O3/TiO2
SiO2	1.00	0.12	-0.35	-0.26	-0.04	-0.75	-0.25	-0.91	-0.20	-0.60	-0.10	-0.05	-0.05	0.42	0.45	0.49	-0.85	-0.53	0.64	0.55	-0.54	-0.79
TiO2	0.12	1.00	0.22	0.70	0.78	0.14	0.50	-0.22	0.19	-0.19	0.50	-0.17	-0.15	0.14	0.17	0.25	-0.21	-0.01	0.24	-0.25	-0.36	-0.55
Al2O3	-0.35	0.22	1.00	0.11	0.39	0.21	0.55	-0.03	0.92	0.58	0.53	-0.48	-0.47	-0.41	-0.40	-0.27	-0.16	0.86	-0.16	-0.84	0.05	-0.04
Fe2O3	-0.26	0.70	0.11	1.00	0.89	0.67	0.56	0.18	0.10	-0.19	0.60	-0.18	-0.16	0.05	0.05	0.16	0.20	-0.04	0.01	-0.31	-0.25	-0.18
FeO	-0.04	0.78	0.39	0.89	1.00	0.47	0.72	-0.16	0.38	-0.22	0.82	-0.33	-0.31	0.14	0.15	0.33	-0.19	0.07	0.21	-0.41	-0.49	-0.46
MnO	-0.75	0.14	0.21	0.67	0.47	1.00	0.44	0.69	0.11	0.24	0.52	-0.02	-0.01	-0.22	-0.25	-0.22	0.66	0.25	-0.34	-0.45	0.23	0.51
MgO	-0.25	0.50	0.55	0.56	0.72	0.44	1.00	0.07	0.49	-0.17	0.77	-0.31	-0.30	0.14	0.13	0.32	-0.00	0.18	0.01	-0.56	-0.47	-0.13
CaO	-0.91	-0.22	-0.03	0.18	-0.16	0.69	0.07	1.00	-0.16	0.41	-0.13	0.25	0.25	-0.29	-0.33	-0.42	0.99	0.22	-0.62	-0.24	0.57	0.89
Na2O	-0.20	0.19	0.92	0.10	0.38	0.11	0.49	-0.16	1.00	0.45	0.46	-0.78	-0.76	-0.52	-0.52	-0.29	-0.27	0.81	-0.28	-0.75	-0.07	-0.14
K2O	-0.60	-0.19	0.58	-0.19	-0.22	0.24	-0.17	0.41	0.45	1.00	-0.16	-0.10	-0.09	-0.74	-0.74	-0.84	0.34	0.89	-0.44	-0.60	0.82	0.49
P2O5	-0.10	0.50	0.53	0.60	0.82	0.52	0.77	-0.13	0.46	-0.16	1.00	-0.24	-0.23	0.21	0.20	0.37	-0.21	0.16	0.29	-0.44	-0.48	-0.29
(CIW)	-0.05	-0.17	-0.48	-0.18	-0.33	-0.02	-0.31	0.25	-0.78	-0.10	-0.24	1.00	1.00	0.58	0.59	0.28	0.29	-0.45	0.39	0.49	0.19	0.23
(CPA)	-0.05	-0.15	-0.47	-0.16	-0.31	-0.01	-0.30	0.25	-0.76	-0.09	-0.23	1.00	1.00	0.58	0.60	0.28	0.29	-0.44	0.40	0.48	0.18	0.23
(CIA)	0.42	0.14	-0.41	0.05	0.14	-0.22	0.14	-0.29	-0.52	-0.74	0.21	0.58	0.58	1.00	1.00	0.94	-0.27	-0.74	0.65	0.62	-0.69	-0.38
(PIA)	0.45	0.17	-0.40	0.05	0.15	-0.25	0.13	-0.33	-0.52	-0.74	0.20	0.59	0.60	1.00	1.00	0.93	-0.30	-0.74	0.69	0.60	-0.68	-0.42
(CIX)	0.49	0.25	-0.27	0.16	0.33	-0.22	0.32	-0.42	-0.29	-0.84	0.37	0.28	0.28	0.94	0.93	1.00	-0.41	-0.69	0.60	0.51	-0.89	-0.53
(ICV)	-0.85	-0.21	-0.16	0.20	-0.19	0.66	-0.00	0.99	-0.27	0.34	-0.21	0.29	0.29	-0.27	-0.30	-0.41	1.00	0.11	-0.59	-0.14	0.57	0.87
(WIP)	-0.53	-0.01	0.86	-0.04	0.07	0.25	0.18	0.22	0.81	0.89	0.16	-0.45	-0.44	-0.74	-0.74	-0.69	0.11	1.00	-0.43	-0.80	0.50	0.27
(W)	0.64	0.24	-0.16	0.01	0.21	-0.34	0.01	-0.62	-0.28	-0.44	0.29	0.39	0.40	0.65	0.69	0.60	-0.59	-0.43	1.00	0.37	-0.44	-0.63
SiO2/Al2O3	0.55	-0.25	-0.84	-0.31	-0.41	-0.45	-0.56	-0.24	-0.75	-0.60	-0.44	0.49	0.48	0.62	0.60	0.51	-0.14	-0.80	0.37	1.00	-0.29	-0.18
K2O/Al2O3	-0.54	-0.36	0.05	-0.25	-0.49	0.23	-0.47	0.57	-0.07	0.82	-0.48	0.19	0.18	-0.69	-0.68	-0.89	0.57	0.50	-0.44	-0.29	1.00	0.68
Al2O3/TiO2	-0.79	-0.55	-0.04	-0.18	-0.46	0.51	-0.13	0.89	-0.14	0.49	-0.29	0.23	0.23	-0.38	-0.42	-0.53	0.87	0.27	-0.63	-0.18	0.68	1.00

SedWeather: A web application framework to visualize Chemical Weathering of Clastic Sediments¶

Flowchart¶

Packages installation¶

Packages import¶

Data loading¶

Weathering Indices (WIs) calculation and Compositional Space Diagrams¶

Molar calculation¶

CaO* calculation¶

Chemical Indices calculation¶

Weathering Indices (WIs) formulae used:¶

Chemical Index of Weathering (CIW) after Harnois, 1988:¶

$ CIW = 100 * \frac {Al_2O_3} {Al_2O_3 + CaO* + Na_2O} $¶

Chemical Proxy of Alteration (CPA) after Buggle et al., 2011:¶

$ CPA = 100 * \frac {Al_2O_3} {Al_2O_3 + Na_2O} $¶

Chemical Index of Alteration (CIA) after Nesbitt and Young, 1982:¶

$ CIA = 100 * \frac {Al_2O_3} {Al_2O_3 + CaO* + Na_2O + K_2O} $¶

Plagioclase Index of Alteration (PIA) after Fedo et al., 1995:¶

$ PIA = 100 * \frac {Al_2O_3 - K_2O} {Al_2O_3 + CaO* + Na_2O - K_2O} $¶

Modified Chemical Index of Alteration (CIX) after Garzanti et al., 2014:¶

$ CIX = 100 * \frac {Al_2O_3} {Al_2O_3 + Na_2O + K_2O} $¶

Index of Compositional Variability (ICV) after Cox et al., 1995:¶

$ ICV = \frac {Fe_2O_3 + K_2O + Na_2O + CaO + MgO + MnO + TiO_2} {Al_2O_3} $¶

Weathering Index of Parker (WIP) after Parker, 1970:¶

$ WIP = 100 * (\frac {2Na_2O} {0.35} + \frac {MgO} {0.9} + \frac {2K_2O} {0.25}+ \frac {CaO*} {0.7} ) $¶

Compositional space diagrams¶

A - CN - K compositional space diagram after Nesbitt and Young, 1982¶

M - F - W compositional space diagramm after Ohta and Arai, 2007¶

A - CNK - FM compositional space diagram after Nesbitt and Young, 1989¶

Data slicing¶

user-defined subcategory and subsubcategory plots¶

Ratios¶

Data statistics¶

Dropping extra variable and subsetting data¶

Boxplots¶

Scatter matrix¶

Correlation matrix¶

Visual representation of correlation matrix of subcategories¶

Correlation diagrams¶

SedWeathering layout design¶